reflect

reflect is a skill for Claude Code from agenticnotetaking/arscontexta. It costs 62 tokens per session (6,589 once invoked), scanned C, original, MIT.

A note-connection skill that finds meaningful relationships between notes and updates MOCs, or maps of content that organize related notes.

In plain words
What is it for?
Use it after creating notes to discover related ideas, add links, update topic maps, and synthesize material around a topic.
Why use it?
It helps turn separate notes into a connected knowledge graph and keeps topic maps up to date.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the arscontexta plugin — 26 skills, 1 agent, 2 hooks shipped together

Good fit Use it after creating notes to discover related ideas, add links, update topic maps, and synthesize material around a topic.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agenticnotetaking/arscontexta/reflect
About the project

Ars Contexta is a Claude Code plugin that creates a personalized knowledge system from a conversation about how someone thinks and works, using connected Markdown files, note-processing skills, hooks, navigation maps, and templates. It is for people who want an owned, persistent second brain tailored to their domain. The catalogue entries are its generated skills, hooks, agent, and plugin workflow.

agenticnotetaking/arscontexta · 3,509 stars · on GitHub · arscontexta.org

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add agenticnotetaking/arscontexta --skill reflect
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code.

Or install arscontexta, the plugin that ships this one along with the rest of its 26 skills, 1 agent, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for reflect

README.md
[![agentmods](https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/reflect/github.svg)](https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect)
Your own site
<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/reflect/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for reflect

Your own site · 80×15
<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,589 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.06589
Opus 5.5 $0.00025 $0.02636
Sonnet 5.5 $0.00012 $0.01318
Haiku 4.5 $0.00006 $0.00659

Measured 1mo ago against content hash 98fef2d260f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade C, and why

reflect scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf "$LOCKDIR"
skill-sources/reflect/SKILL.md · 747 lines

How it starts

The opening of the file, as written. The whole thing — 747 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, platform hints

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.note / vocabulary.note_plural for note type references
    • Use vocabulary.reflect for the process verb in output
    • Use vocabulary.topic_map / vocabulary.topic_map_plural for MOC references
    • Use vocabulary.cmd_reweave for the next-phase suggestion
    • Use vocabulary.inbox for the inbox folder name
  2. ops/config.yaml — processing depth, pipeline chaining

    • processing.depth: deep | standard | quick
    • processing.chaining: manual | suggested | automatic

If these files don't exist, use universal defaults.

Processing depth adaptation:

Depth Connection Behavior
deep Full dual discovery (MOC + semantic search). Evaluate every candidate. Multiple passes. Synthesis opportunity detection. Bidirectional link evaluation for all connections.
standard Dual discovery with top 5-10 candidates. Standard evaluation. Bidirectional check for strong connections only.
quick Single pass — either MOC or semantic search. Accept obvious connections only. Skip synthesis detection.

EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

  • If target contains [[note name]] or note name: find connections for that {vocabulary.note}
  • If target contains --handoff: output RALPH HANDOFF block at end
  • If target is empty: check for recently created {vocabulary.note_plural} or ask which {vocabulary.note}
  • If target is "recent" or "new": find connections for all {vocabulary.note_plural} created today

Execute these steps:

  1. Read the target {vocabulary.note} fully — understand its claim and context
  2. Throughout discovery: Capture which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which candidates you evaluated. This becomes the Discovery Trace — proving methodology was followed, not reconstructed.
  3. Run Phase 0 (index freshness check)
  4. Use dual discovery in parallel:
    • Browse relevant {vocabulary.topic_map}(s) for related {vocabulary.note_plural}
    • Run semantic search for conceptually related {vocabulary.note_plural}
  5. Evaluate each candidate: does a genuine connection exist? Can you articulate WHY?
  6. Add inline wiki-links where connections pass the articulation test
  7. Update relevant {vocabulary.topic_map}(s) with this {vocabulary.note}
  8. If task file in context: update the {vocabulary.reflect} section
  9. Report what was connected and why
  10. If --handoff in target: output RALPH HANDOFF block

Read the full file on GitHub · 747 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 747 lines · 62 tokens per session scan C 98fef2d260f4

Subscribe to this mod's changes

reflect is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,509 stars, last pushed 7mo ago), licensed MIT. It adds 62 tokens to every session and 6,589 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

llm-wiki

Build and maintain an LLM-curated knowledge base from papers, articles, transcripts, notes and project findings. Ingest sources into linked Markdown, answer questions with citations, lint or upgrade a wiki, and preserve useful synthesis across sessions and agents. Also capture verified task experience, consolidate…

praneybehl/llm-wiki-plugin · 130 tokens

migrate

Scan the project and migrate existing documentation into OKF format. Use when the user wants to populate the knowledge base from existing docs, README, schemas, API specs, runbooks, or any structured project knowledge. Trigger on "migrate to okf", "populate the knowledge base", "scan my docs", "convert docs to okf"…

guhcostan/claude-mega-brain · 147 tokens

claude-mega-brain

OKF knowledge navigation. ALWAYS use this skill the moment a block appears in session context — it means the project has documented concepts and you must consult them before answering questions about data, schemas, metrics, APIs, or systems. Also trigger when the user asks about a table, column, metric definition…

guhcostan/claude-mega-brain · 96 tokens

ingest

Create or update a single OKF concept file from raw input. Use when the user wants to document a specific table, metric, API, service, or concept in the knowledge base — even if they say "add this to the okf", "documenta essa tabela", "cria um conceito pra isso", "adiciona no knowledge base", "register this API", or…

guhcostan/claude-mega-brain · 108 tokens

init

Initialize OKF knowledge base files in the current project. Use when the user wants to start documenting their project for claude-mega-brain, or when the user says "init mega brain", "setup the knowledge base", "cria o okf", "inicializa o mega brain", "quero usar o mega brain", "criar a estrutura do knowledge base"…

guhcostan/claude-mega-brain · 89 tokens

query

Progressive retrieval from the Trove index. Use when the user asks what Trove knows about a subsystem, where code lives, or wants context assembled before editing. Runs architecture -> subsystem -> module -> symbol retrieval with a context budget.

anishfyi/trove · 49 tokens